Barnett, S. (1979). *Matrix Methods for Engineers and Scientists*. MacGraw-Hill.

Cortes, C., Haffner, P., & Mohri, M. (2003). Positive Definite Rational Kernels. In *Proceedings of the 16th Annual Conference on Computational Learning Theory and the 7th Kernel Workshop*.

Dearden, R., Friedman, N., & Russell, S. (1998). Bayesian Q-learning. In *Proceedings of AAAI-98/IAAI-98*, (pp. 761–768).

Demaine, E., Hohenberger, S., & Liben-Nowell, D. (2002). Tetris is Hard, Even to Approximate. Technical Report MIT-LCS-TR-865, Massachussets Institue of Technology, Boston.

Google ScholarDeshpande, M., Kuramochi, M., & Karypis, G. (2002). Automated Approaches for Classifying Structures. In *Proceedings of the 2nd ACM SIGKDD Workshop on Data Mining in Bioinformatics*.

Diestel, R. (2000). *Graph Theory*. Springer-Verlag.

Dietterich, T., & Wang, X. (2002). Batch value function approximation via support vectors. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.),

*Advances in Neural Information Processing Systems*, vol. 14, Cambridge, MA, The MIT Press.

Google ScholarDriessens, K., & Džeroski, S. (2002). Integrating experimentation and guidance in relational reinforcement learning. In C. Sammut, & A. Hoffmann (Eds.), *Proceedings of the Nineteenth International Conference on Machine Learning* (pp. 115–122). Morgan Kaufmann Publishers, Inc.

Driessens, K., & Džeroski, S. (2004). Integrating guidance into relational reinforcement learning.

*Machine Learning*,

*57*, 271–304.

MATHCrossRefGoogle ScholarDriessens, K., & Ramon, J. (2003). Relational instance based regression for relational reinforcement learning. In *Proceedings of the Twentieth International Conference on Machine Learning* (pp. 123–130). AAAI Press.

Driessens, K., Ramon, J., & Blockeel, H. (2001). Speeding up Relational Reinforcement Learning Through the Use of an Incremental First Order Decision Tree Learner. In L. De Raedt, & P. Flach (Eds.), *Proceedings of the 13th European Conference on Machine Learning*, vol. 2167 of *Lecture Notes in Artificial Intelligence* (pp. 97–108). Springer-Verlag.

Džeroski, S., De Raedt, L., & Blockeel, H. (1998). Relational reinforcement Learning. In *Proceedings of the 15th International Conference on Machine Learning* (pp. 136–143). Morgan Kaufmann.

Engel, Y., Mannor, S., & Meir, R. (2003). Bayes meets Bellman: The gaussian process approach to temporal difference learning. In *Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003)* (pp. 154–161). Morgan Kaufmann.

Gärtner, T. (2002). Exponential and Geometric Kernels for Graphs. In *NIPS Workshop on Unreal Data: Principles of Modeling Nonvectorial Data*.

Gärtner, T. (2003). A survey of kernels for structured data.

*SIGKDD Explorations*,

*5*(1), 49–58.

Google ScholarGärtner, T., Driessens, K., & Ramon, J. (2003a). Graph kernels and Gaussian processes for relational reinforcement learning. In *Inductive Logic Programming, 13th International Conference, ILP 2003, Proceedings*, vol. 2835 of *Lecture Notes in Computer Science* (pp. 146–163). Springer.

Gärtner, T., Flach, P., & Wrobel, S. (2003b). On graph kernels: Hardness Results and Efficient Alternatives. In M. W. B. Schölkopf (Ed.), *Proceedings of the 16th Annual Conference on Computational Learning Theory and the 7th Kernel Workshop* (129–143).

Gibbs, M. (1997). Bayesian Gaussian Processes for Regression and Classification. Ph.D. thesis, University of Cambridge.

Golub, G. H., & Van Loan, C. F. (1996). *Matrix computations*. Johns Hopkins Series in the Mathematical Sciences. The Johns Hopkins University Press.

Graepel, T. (2002). PAC-Bayesian Pattern Classification with Kernels. Ph.D. thesis, TU Berlin.

Horvath, T., Gärtner, T., & Wrobel, S. (2004). Cyclic Pattern Kernels for Predictive Graph Mining. In *Proceedings of the International Conference on Knowledge Discovery and Data Mining*.

Imrich, W., & Klavžar, S. (2000). *Product Graphs: Structure and Recognition*. John Wiley.

Kaelbling, L., Littman, M., & Moore, A. (1996). Reinforcement learning: A survey.

*Journal of Artificial Intelligence Research*,

*4*, 237–285.

Google ScholarKashima, H., & Inokuchi, A. (2002). Kernels for Graph Classification. In *ICDM Workshop on Active Mining*.

Kashima, H., Tsuda, K., & Inokuchi, A. (2003). Marginalized kernels Between Labeled Graphs. In *Proceedings of the 20th International Conference on Machine Learning*.

Korte, B., & Vygen, J. (2002). *Combinatorial Optimization: Theory and Algorithms*. Springer-Verlag.

Kuramochi, M., & Karypis, G. (2001). Frequent subgraph discovery. In *Proceedings of the IEEE International Conference on Data Mining*.

MacKay, D. (1997a) Introduction to Gaussian processes. Aavailable at

http://wol.ra.phy.cam.ac.uk/mackay.

MacKay, D. J. C. (1997b). Introduction to Gaussian processes. Available at

http://wol.ra.phy.cam.ac.uk/mackay.

Mitchell, T. (1997). *Machine Learning*. McGraw-Hill.

Ormoneit, D., & Sen, S. (2002). Kernel-based reinforcement learning.

*Machine Learning*,

*49*, 161–178.

MATHCrossRefGoogle ScholarRasmussen, C. E., & Kuss, M. (2004). Gaussian Processes in Reinforcement Learning. In *Advances in Neural Information Processing Systems*, vol. 16. MIT Press.

Rifkin, R. M. (2002). Everything old is new again: A fresh Look at Historical Approaches to Machine Learning. Ph.D. thesis, MIT.

Saunders, C., Gammerman, A., & Vovk, v. (1998). Ridge Regression Learning Algorithm in Dual Variables. In *Proceedings of the Fifteenth International Conference on Machine Learning*. Morgan Kaufmann.

Schaal, S., Atkeson, C. G., & Vijayakumar, S. (2000). Real-Time Robot Learning with Locally Weighted Statistical Learning. In

*Proceedings of the IEEE International Conference on Robotics and Automation* (pp. 288–293). IEEE Press, Piscataway, N.J.

Google ScholarSchölkopf, B., & Smola, A. J. (2002). *Learning with kernels*. MIT Press.

Smart, W. D., & Kaelbling, L. P. (2000). Practical Reinforcement Learning in Continuous Spaces. In *Proceedings of the 17th International Conference on Machine Learning* (pp. 903–910). Morgan Kaufmann.

Sutton, R., & Barto, A. (1998).

*Reinforcement Learning: An introduction*. Cambridge, MA: The MIT Press.

Google ScholarVapnik, V. (1995). *The Nature of Statistical Learning Theory*. Springer-Verlag.

Watkins, C. (1989). Learning from Delayed Rewards. Ph.D. thesis, King’s College, Cambridge.